Objective:To explore the value of prenatal three-dimensional ultrasonography（3DUS）in displaying auricular morphotyping and dimensions for predicting congenital aural atresia（CAA）.Methods:A retrospective collection of 227 fetuses who underwent ultrasound scans and retained auricular 3DUS volumes from January 2018 to December 2023 at the First Affiliated Hospital of Sun Yat-sen University was conducted. Fetuses were divided into two groups：a CAA group（52 fetuses，62 auricles）and a non-CAA group（175 fetuses，202 auricles），based on the presence or absence of external auditory canal identified through postnatal examination. According to 3DUS auricular contour and presence or absence of the concha，the auricles were divided into 4 types：type Ⅰ，C-shaped auricle with a concha；type Ⅱ，Irregular auricle with a concha；type Ⅲ，C-shaped auricle without a concha；type Ⅳ，Irregular auricle without a concha. And auricular length（AL）and width（AW）were measured to calculate the product of the auricular length and width（ALW）. Normal reference ranges for ALW from the non-CAA group were developed. Differences of the auricular morphotyping and Z-score of ALW（ALWZ）were compared between the two groups. Receiver operating characteristic（ROC）curves were used to evaluate the diagnostic efficiency of auricular morphotyping，ALWZ and the regression model. A Logistic regression model for CAA based on auricular morphotyping and ALWZ were established.Results:The auricular morphotyping and ALWZ between the two groups were different statistically（both P<0.05）. The AUC of the auricular morphotyping and ALWZ predicting CAA were 0.960（95% CI = 0.923 - 0.997）and 0.975（95% CI = 0.959 - 0.991）individually. Formula for CAA prediction model combining the two indicators（5.379 × morphotyping - 2.386 × ALWZ - Conclusions:The auricular morphotyping and dimensions can effectively predict CAA.

The integration of artificial intelligence（AI）with prenatal ultrasound is driving a paradigm shift in obstetric imaging，transitioning from an experience-based approach to a data-driven model and gradually transforming traditional clinical practices. This review systematically examines AI-driven advancements

Objective:To develop a deep learning-based artificial intelligence system for assessing image quality in early pregnancy ultrasound，and to evaluate its performance in anatomical structure identification and quality control.Methods:A retrospective study was conducted by collecting 17 910 static ultrasound images of 8 quality-control planes from fetuses at 11 to 13 +6 weeks of gestation who underwent routine first-trimester ultrasound examinations at the First Affiliated Hospital of Sun Yat-sen University from June 2018 to June 2024. The dataset was divided into a training set（12 536 images），a test set（3 582 images），and a validation set（1 792 images）in a 7∶2∶1 ratio to develop a prenatal-screening artificial intelligence system（PSAIS）and to evaluate its performance in the automatic recognition and quality control of standard planes during early pregnancy. The average precision and mean average precision（mAP）were used to measure the model's ability to recognize the anatomical structures on each plane. Intraclass correlation coefficient（ICC）and Kappa statistics were used to assess the consistency between PSAIS and expert-level sonographers in both plane image quality assessment and standardization. The efficiency of PSAIS was also compared to manual quality control. Results:In the test set，the mAP values for recognizing the anatomical structures of the 8 quality-control planes all exceeded 0.800. In the validation set，PSAIS demonstrated moderate to good agreement with two experts in image quality evaluation：the ICC ranged from 0.713 to 0.843 for one expert and 0.678 to 0.788 for the other，while the Kappa values ranged from 0.590 to 0.768 and 0.530 to 0.702，respectively. In terms of plane standardization scoring，PSAIS showed particularly high agreement with expert ratings on the transventricular view（compliance rate 94.6%，Kappa=0.860）and the four-chamber cardiac view with blood flow（compliance rate 94.1%，Kappa=0.778），with agreement above 70% for the remaining planes. Compared with manual quality-control，PSAIS significantly increased processing speed：the total processing time was only 413 seconds，markedly less than the 77 008 seconds and 94 918 seconds required for manual QC（ P<0.001）. Conclusions:The PSAIS system performs well in recognizing and controlling the quality of standard ultrasound planes in early pregnancy，demonstrating high consistency with expert evaluations and significantly improved processing efficiency. It has potential application value in enhancing the quality and efficiency of early pregnancy screening.

Congenital orofacial cleft, the most common birth defect in the maxillofacial region, exhibits a wide range of prognosis depending on the severity of deformity and underlying etiology. Non-syndromic congenital orofacial clefts typically present with milder deformities and more favorable treatment outcomes, whereas syndromic congenital orofacial clefts often manifest with concomitant organ abnormalities, which pose greater challenges for treatment and result in poorer prognosis. This consensus provides an elaborate classification system for varying degrees of orofacial clefts along with corresponding diagnostic and therapeutic guidelines. Results serve as a crucial resource for families to navigate prenatal screening results or make informed decisions regarding treatment options while also contributing significantly to preventing serious birth defects within the development of population.
Humans ; Cleft Lip/diagnosis* ; Cleft Palate/diagnosis* ; Consensus ; Prenatal Diagnosis ; Female

Objective:To explore the value of prenatal three-dimensional ultrasonography（3DUS）in displaying auricular morphotyping and dimensions for predicting congenital aural atresia（CAA）.Methods:A retrospective collection of 227 fetuses who underwent ultrasound scans and retained auricular 3DUS volumes from January 2018 to December 2023 at the First Affiliated Hospital of Sun Yat-sen University was conducted. Fetuses were divided into two groups：a CAA group（52 fetuses，62 auricles）and a non-CAA group（175 fetuses，202 auricles），based on the presence or absence of external auditory canal identified through postnatal examination. According to 3DUS auricular contour and presence or absence of the concha，the auricles were divided into 4 types：type Ⅰ，C-shaped auricle with a concha；type Ⅱ，Irregular auricle with a concha；type Ⅲ，C-shaped auricle without a concha；type Ⅳ，Irregular auricle without a concha. And auricular length（AL）and width（AW）were measured to calculate the product of the auricular length and width（ALW）. Normal reference ranges for ALW from the non-CAA group were developed. Differences of the auricular morphotyping and Z-score of ALW（ALWZ）were compared between the two groups. Receiver operating characteristic（ROC）curves were used to evaluate the diagnostic efficiency of auricular morphotyping，ALWZ and the regression model. A Logistic regression model for CAA based on auricular morphotyping and ALWZ were established.Results:The auricular morphotyping and ALWZ between the two groups were different statistically（both P<0.05）. The AUC of the auricular morphotyping and ALWZ predicting CAA were 0.960（95% CI = 0.923 - 0.997）and 0.975（95% CI = 0.959 - 0.991）individually. Formula for CAA prediction model combining the two indicators（5.379 × morphotyping - 2.386 × ALWZ - Conclusions:The auricular morphotyping and dimensions can effectively predict CAA.

The integration of artificial intelligence（AI）with prenatal ultrasound is driving a paradigm shift in obstetric imaging，transitioning from an experience-based approach to a data-driven model and gradually transforming traditional clinical practices. This review systematically examines AI-driven advancements

Objective:To develop a deep learning-based artificial intelligence system for assessing image quality in early pregnancy ultrasound，and to evaluate its performance in anatomical structure identification and quality control.Methods:A retrospective study was conducted by collecting 17 910 static ultrasound images of 8 quality-control planes from fetuses at 11 to 13 +6 weeks of gestation who underwent routine first-trimester ultrasound examinations at the First Affiliated Hospital of Sun Yat-sen University from June 2018 to June 2024. The dataset was divided into a training set（12 536 images），a test set（3 582 images），and a validation set（1 792 images）in a 7∶2∶1 ratio to develop a prenatal-screening artificial intelligence system（PSAIS）and to evaluate its performance in the automatic recognition and quality control of standard planes during early pregnancy. The average precision and mean average precision（mAP）were used to measure the model's ability to recognize the anatomical structures on each plane. Intraclass correlation coefficient（ICC）and Kappa statistics were used to assess the consistency between PSAIS and expert-level sonographers in both plane image quality assessment and standardization. The efficiency of PSAIS was also compared to manual quality control. Results:In the test set，the mAP values for recognizing the anatomical structures of the 8 quality-control planes all exceeded 0.800. In the validation set，PSAIS demonstrated moderate to good agreement with two experts in image quality evaluation：the ICC ranged from 0.713 to 0.843 for one expert and 0.678 to 0.788 for the other，while the Kappa values ranged from 0.590 to 0.768 and 0.530 to 0.702，respectively. In terms of plane standardization scoring，PSAIS showed particularly high agreement with expert ratings on the transventricular view（compliance rate 94.6%，Kappa=0.860）and the four-chamber cardiac view with blood flow（compliance rate 94.1%，Kappa=0.778），with agreement above 70% for the remaining planes. Compared with manual quality-control，PSAIS significantly increased processing speed：the total processing time was only 413 seconds，markedly less than the 77 008 seconds and 94 918 seconds required for manual QC（ P<0.001）. Conclusions:The PSAIS system performs well in recognizing and controlling the quality of standard ultrasound planes in early pregnancy，demonstrating high consistency with expert evaluations and significantly improved processing efficiency. It has potential application value in enhancing the quality and efficiency of early pregnancy screening.

Objective:To summarize the prenatal sonographic characteristics of affected joints of fetal arthrogryposis multiplex congenita (AMC) by comparing the ultrasonographic features and the postnatal pathological manifestations.Methods:The cases of AMC detected by antenatal ultrasound and confirmed by postnatal pathology were collected in the First Affiliated Hospital of Sun Yat-Sen University and Kaiping Central Hospital from January 2015 to June 2020. The differences between prenatal ultrasonic manifestations, types of affected joints and postnatal pathological features were analyzed. And the different involvements of joints in AMC cases with or without other system abnormalities were also explored separately.Results:A total of 31 cases of AMC were included, in which 11 cases were with other system abnormalities and 20 cases without. No significant difference was observed in number of affected joints between these two groups ( P>0.05). The prenatal sonogram features were completely consistent with the postnatal pathological manifestations in 21 (21/31, 67.7%) cases. Among 31 cases, the involvement rates of joints were: interphalangeal joints of fingers (23/31, 74.2%), knee joints (20/31, 64.5%) and ankle joints (19/31, 61.3%), temporomandibular joint (11/31, 35.5%), wrists (11/31, 35.5%), elbow joints (10/31, 32.3%), interphalangeal joints of toes (6/31, 19.4%), spinal joints (2/31, 6.5%), shoulder joint (1/31, 3.2%) and hip joint (1/31, 3.2%), respectively. The coincidence rates of prenatal ultrasound in involved joints were: interphalangeal joints of fingers (100%), ankles (100%), spines (100%), hips (100%), wrists (90.9%), knees (75.0%), elbows (70.0%), jaws (54.5%), interphalangeal joints of toes (50.0%), and shoulders (0), respectively. Conclusions:When postural abnormalities of fetal upper and lower extremities are detected by prenatal ultrasound screening, especially overlapping fingers, extended knee and club foot, AMC should be kept on alert. Simultaneously, other joints should be carefully scanned to improve the prenatal detection rate of AMC.

Objective To analyze the ultrasonic features ,associated malformations and combined genetic abnormalities of microphthalmia . Methods The characteristics of 15 cases of fetal microphthalmia were retrospectively analyzed . And the proportion of fetal microphthalmia associated malformations were further assessed according to the different organ system . Results All the orbital diameters of affected eyes of the 15 cases were less than the 5th centile of normal fetal orbital diameter corresponding to gestational age . In which ,26 .67% ( 4/15 ) fetuses had additional ocular defects ,and 66 .67% ( 10/15 ) were diagnosis with extrocular defects ,including 20 .00% ( 3/15) with central nervous system defects ,13 .33% ( 2/15) with orofacial defects ,26 .67% ( 4/15) with cardiac defect ,13 .33% ( 2/15) with limb defect ,33 .33% ( 2/15) with urogenital defect and 40 .00% ( 6/15 ) with abnormal ultrasonographic soft markers . And the proportion of fetal microphthalmia associated extrocular defects showed no significant difference ( P ＝ 0 .502 ) . Conclusions Fetal microphthalmia is frequently associated with random and sporadic occurrence of extrocular defects

Objective: To analyze the ultrasonic features, associated malformations and combined genetic abnormalities of microphthalmia . Methods: The characteristics of 15 cases of fetal microphthalmia were retrospectively analyzed. And the proportion of fetal microphthalmia associated malformations were further assessed according to the different organ system. Results: All the orbital diameters of affected eyes of the 15 cases were less than the 5th centile of normal fetal orbital diameter corresponding to gestational age. In which, 26.67%(4/15) fetuses had additional ocular defects, and 66.67%(10/15) were diagnosis with extrocular defects, including 20.00%(3/15) with central nervous system defects, 13.33%(2/15) with orofacial defects, 26.67% (4/15) with cardiac defect, 13.33%(2/15) with limb defect, 33.33% (2/15) with urogenital defect and 40.00%(6/15) with abnormal ultrasonographic soft markers. And the proportion of fetal microphthalmia associated extrocular defects showed no significant difference(P=0.502). Conclusions Fetal microphthalmia is frequently associated with random and sporadic occurrence of extrocular defects